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187 results for “Language Data”
Data for Investigating the Technical Debt in Procedural Model Transformation Languages
<p>The content presents the data for Investigating the Technical Debt in Procedural Model Transformation Languages </p>
Tokenized and POS-Tagged Khmer Data of the Asian Language Treebank Project
<p>* Introduction</p> <p>This is the Khmer ALT of the Asian Language Treebank (ALT) Corpus. English texts sampled from English Wikinews were available under a Creative Commons Attribution 2.5 License.</p> <p>Please refer to<br> http://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/index.html<br> for an introduction of the ALT project.</p> <p>Khmer ALT has been developed by NICT and NIPTICT. The license of Khmer ALT is</p> <p>Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License<br> https://creativecommons.org/licenses/by-nc-sa/4.0/</p> <p><br> * Contents</p> <p>- data_km.km-[tok|tag].nova : tokenized/POS-tagged Khmer sentences by the nova annotation system<br> # based on the following two guildelines<br> # http://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/Khmer-annotation-guideline.pdf<br> # http://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/Khmer-annotation-guideline-supplementary.pdf</p> <p><br> * Disclaimer</p> <p>[1] The content of the selected English Wikinews articles have been translated for this corpus. English texts sampled from English Wikinews were available under a Creative Commons Attribution 2.5 License. Users of the corpus are requested to take careful consideration when encountering any instances of defamation, discriminatory terms, or personal information that might be found within the corpus. Users of the corpus are advised to read Terms of Use in https://en.wikinews.org/wiki/Main_Page carefully to ensure proper usage.</p> <p>[2] NICT bears no responsibility for the contents of the corpus and the lexicon and assumes no liability for any direct or indirect damage or loss whatsoever that may be incurred as a result of using the corpus or the lexicon.</p> <p>[3] If any copyright infringement or other problems are found in the corpus or the lexicon, please contact us at alt-info[at]khn[dot]nict[dot]go[dot]jp. We will review the issue and undertake appropriate measures when needed.</p>
Data for "Learning the language of viral evolution and escape"
<p>Training data from:</p> <ul> <li>Influenza A HA protein sequences from the NIAID Influenza Research Database (IRD) (<a href="http://www.fludb.org">http://www.fludb.org</a>)</li> <li>HIV-1 Env protein sequences from the Los Alamos National Laboratory (LANL) HIV database (<a href="https://www.hiv.lanl.gov">https://www.hiv.lanl.gov</a>)</li> <li><em>Coronavidae</em> spike protein sequences from the Virus Pathogen Resource (ViPR) database (<a href="https://www.viprbrc.org/brc/home.spg?decorator=corona">https://www.viprbrc.org/brc/home.spg?decorator=corona</a>)</li> <li>SARS-CoV-2 Spike protein sequences from NCBI Virus (<a href="https://www.ncbi.nlm.nih.gov/labs/virus/vssi/">https://www.ncbi.nlm.nih.gov/labs/virus/vssi/</a>)</li> <li>SARS-CoV-2 Spike and other Betacoronavirus spike protein sequences from GISAID (<a href="https://www.gisaid.org/">https://www.gisaid.org/</a>)</li> </ul> <p>Datasets for fitness and escape validation:</p> <ul> <li>Fitness single-residue DMS of HA H1 WSN33 from Doud and Bloom (2016)</li> <li>Fitness combinatorial DMS of antigenic site B in six HA H3 strains from Wu et al. (2020)</li> <li>Fitness single-residue DMS of Env BF520 and BG505 from Haddox et al. (2018)</li> <li>ACE2 binding affinity combinatorial DMS of Spike from Starr et al. (2020)</li> <li>Escape single-residue DMS of HA H1 WSN33 from Doud et al. (2018)</li> <li>Escape single-residue DMS of HA H3 Perth09 from Lee et al. (2019)</li> <li>Escape single-residue DMS of Env BG505 from Dingens et al. (2019)</li> <li>Escape mutations of Spike from Baum et al. (2020)</li> <li>Escape single-residue DMS of Spike from Greaney et al. (2020)</li> </ul>
data set related to article Which linguistic measures distinguish transient from persistent language problems in Late Talkers from 2 to 4 years? A study on Italian speaking children
<p>This record contains raw data related to article Which linguistic measures distinguish transient from persistent language problems in Late Talkers from 2 to 4 years? A study on Italian speaking children</p>
Usability Evaluation Process for Domain-Specific Languages - Documents and survey data
<p>Usability Evaluation Process for Domain-Specific Languages - Documents and survey data</p>
Raw data on sample languages
<p>Raw data on sample languages</p>
Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 1 September 2017
<p>Open Language Archive Community (OLAC) Nightly Data Dump (XML) from 1 September 2017</p>
Research data supporting article on "Language and Design Evolution of the OpenMC Monte Carlo Particle Transport Code"
<p>This file contains an archive of all research data that supports the article on "Language and Design Evolution of the OpenMC Monte Carlo Particle Transport Code".</p>
Replications Package for "Understanding the Dynamics of License Selection in OSS: A Data-Driven Exploration Across Different Programming Languages"
<p>Replications Package for "Understanding the Dynamics of License Selection in OSS: A Data-Driven Exploration Across Different Programming Languages"<br><br>Includes datasets and R code.</p>
Data set to train a natural language classifier able to differentiate between 15 topics relevant to biodiversity informatics
<p><strong>Scope and size</strong><br> This data set is used to train a natural language processing classifier. The classifier shall be able to differentiate between 15 topics relevant to biodiversity informatics. The list of relevant topics was adapted from Searls (2012).</p> <p>The data set was split into training data, testing data (for tweaking and unit-testing the classifier) and validation data. Each data set is stored as PDF files in a separate directory.</p> <ul> <li>Training data (5494 pages)</li> <li>Test data (977 pages)</li> <li>Validation data (215 pages)</li> </ul> <p><strong>Data sources and licenses</strong><br> Details about the licenses for each data set can be found in the corresponding directories.</p> <ul> <li>Training data was compiled from MIT OpenCourseWare resources provided by MIT under a Creative Commons BY-NC-SA License.</li> <li>Testing data was compiled from MIT OpenCourseWare exams, provided by MIT under a Creative Commons License BY-NC-SA.</li> <li>Validation data was compiled from Wikipedia, provided under a Creative Commons License by Wikipedia editors and contributors.</li> </ul> <p><strong>Topic references</strong><br> Each topic references one or more MIT OpenCourseWare courses:</p> <ul> <li>Algorithms (Demaine, and Devadas, 2011)</li> <li>Artificial Intelligence (Winston, 2010)</li> <li>Building Dynamic Websites (Abelson, and Greenspun, 2003)</li> <li>Computational Biology (Kellis, 2015)</li> <li>Computer Graphics (Matusik, and Durand, 2012)</li> <li>Computer Science and Programming (Bell, Grimson, and Guttag, 2016)</li> <li>Databases (Madden, Morris, Stonebraker, and Curino, 2010)</li> <li>Data Structures (Demaine, 2012)</li> <li>Digital Image Processing (Clifford, Fisher, Greenberg, and Wells, 2007; Golland, 2005)</li> <li>Machine Learning (Singh, Jaakkola, and Mohammad, 2006)</li> <li>Machine Structures (Morris, and Madden, 2009)</li> <li>Natural Language Processing (Berwick, 2003; Collins, and Barzilay, 2005)</li> <li>Parallel Computing (Edelman, 2011)</li> <li>Software Engineering (Jackson, and Devadas, 2005)</li> <li>Structure and Interpretation of Computer Programs (Miller, and Goldman, 2016).</li> </ul> <p><strong>References</strong></p> <p>Harold Abelson, and Philip Greenspun. 6.171 Software Engineering for Web Applications. Fall 2003. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Ana Bell, Eric Grimson, and John Guttag. 6.0001 Introduction to Computer Science and Programming in Python. Fall 2016. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Robert Berwick. 6.863J Natural Language and the Computer Representation of Knowledge. Spring 2003. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Gari Clifford, John Fisher, Julie Greenberg, and William Wells. HST.582J Biomedical Signal and Image Processing. Spring 2007. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Michael Collins, and Regina Barzilay. 6.864 Advanced Natural Language Processing. Fall 2005. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Erik Demaine. 6.851 Advanced Data Structures. Spring 2012. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Erik Demaine, and Srini Devadas. 6.006 Introduction to Algorithms. Fall 2011. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Alan Edelman. 18.337J Parallel Computing. Fall 2011. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Polina Golland. 6.881 Representation and Modeling for Image Analysis. Spring 2005. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Daniel Jackson, and Srini Devadas. 6.170 Laboratory in Software Engineering. Fall 2005. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Manolis Kellis. 6.047 Computational Biology. Fall 2015. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Robert Miller, and Max Goldman. 6.005 Software Construction. Spring 2016. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Samuel Madden, Robert Morris, Michael Stonebraker, and Carlo Curino. 6.830 Database Systems. Fall 2010. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Wojciech Matusik, and Frédo Durand. 6.837 Computer Graphics. Fall 2012. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Morris, Robert, and Madden, Samuel. 6.033 Computer System Engineering. Spring 2009. Massachusetts Institute of Technology: MIT OpenCourseWare, http://hdl.handle.net/1721.1/118791. License: Creative Commons BY-NC-SA.</p> <p>David B. Searls. An online bioinformatics curriculum. 2012. PLoS computational biology, 8(9), p.e1002632.</p> <p>Rohit Singh, Tommi Jaakkola, and Ali Mohammad. 6.867 Machine Learning. Fall 2006. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p>Patrick Winston. 6.034 Artificial Intelligence. Fall 2010. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.</p> <p> </p>
data for: Inconsistencies Detection in Natural Language Requirements using ChatGPT: a Preliminary Evaluation
<p>Supplementary material for the paper: Inconsistencies Detection in Natural Language Requirements using ChatGPT: a Preliminary Evaluation</p> <p>In the "gold" sheets there are the requirements: originals requirements are marked with "1" in the third column, mutants are marked with "0". </p>
Raw data for Large Language Models Reflect Human Citation Patterns with a Heightened Citation Bias
Open the record for dataset details and reuse information.
Collection of Clinical Data and Specimens for Research in Hearing, Balance, Taste, Smell, Voice, Speech, and Language Disorders.
ClinicalTrials.gov study NCT05670496. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Neural Correlates of Language Control in Bilinguals. Functional MRI and Stimulation Mapping Data in Awake Surgery.
ClinicalTrials.gov study NCT02391493. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Pre- and Post- Descriptive Data of Language Outcomes in Deaf Children
ClinicalTrials.gov study NCT06724523. IPD Sharing: YES. Countries: 1. Publications: 0.
A Vision-Language Foundation Model for Brain Disease Diagnosis From Multimodal Data
ClinicalTrials.gov study NCT07126821. IPD Sharing: NO. Countries: 1. Publications: 0.
Annotation Scheme for Continuous Data in Swiss German Sign Language
<p>Annotation scheme described in the paper "Advancing Annotation for Continuous Data in Swiss German Sign Language" to annotate sentences in Swiss German Sign Language. The annotations are in both German and English.</p>
WMT-SLT SRF: Training data for the WMT shared task on sign language translation (videos, subtitles)
<p>These are Standard German daily news (Tagesschau) and Swiss German weather forecast (Meteo) episodes broadcast and interpreted into Swiss German Sign Language by hearing interpreters (among them, children of Deaf adults, CODA) via Swiss National TV (Schweizerisches Radio und Fernsehen, SRF) (<a href="https://www.srf.ch/play/tv/sendung/tagesschau-in-gebaerdensprache?id=c40bed81-b150-0001-2b5a-1e90e100c1c0">https://www.srf.ch/play/tv/sendung/tagesschau-in-gebaerdensprache?id=c40bed81-b150-0001-2b5a-1e90e100c1c0</a>). For a more extended description of the data, visit <a href="https://www.wmt-slt.com/data">https://www.wmt-slt.com/data</a>.<br> <br> </p>
CASSIA: a multi-agent large language model for reference free, interpretable, and automated cell annotation of single-cell RNA-sequencing data
GEO Series GSE307976. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Data from: experiment using Large Language Models for unit testing generation
<p>IMPORTANT: Upon acceptance, all data will be available on Zenodo.org, and the link updated here.</p><p><br>This dataset represents the anonymized information from: background check form, experiment feedback form and test files produced by experiment participants</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.